SS2 (for AG Hefendehl): Integration
Desirée Brösamle
Dec 2021
Last updated: 2023-07-14
Checks: 6 1
Knit directory: scSeq_Hefendehl/
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Preprocessing
QC metrics from AG Hefendehl (Stroke)
Combined datasets and filter cells that have unique feature counts (gene number) over 7000 or less than 200 and cells with >5% mitochondrial counts.
# Visualize QC metrics as a violin plot
Idents(plates_hefendehl) <- "Plate"
VlnPlot(plates_hefendehl, features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.rp"),
group.by = "Plate", ncol = 4)
SampleMeta=plates_hefendehl@meta.data
cormt<- cor(SampleMeta$nCount_RNA, SampleMeta$percent.mt, method="spearman")
corfeat<- cor(SampleMeta$nCount_RNA, SampleMeta$nFeature_RNA, method="spearman")
plot1 <- ggplot(plates_hefendehl@meta.data,aes(x=nCount_RNA, y=percent.mt, col=Plate))+
geom_point()+theme_classic()+geom_abline(slope = 0, intercept = 5)+labs(title=cormt)
plot2 <- ggplot(plates_hefendehl@meta.data,aes(x=nCount_RNA, y=nFeature_RNA, col=Plate))+geom_point()+theme_classic()+geom_abline(slope = 0, intercept = 7000)+
ggtitle(corfeat)
plot1 | plot2
| Version | Author | Date |
|---|---|---|
| eaa373d | Andreas Chiocchetti | 2023-02-16 |
Perform integrated analysis
| Version | Author | Date |
|---|---|---|
| eaa373d | Andreas Chiocchetti | 2023-02-16 |
| Version | Author | Date |
|---|---|---|
| eaa373d | Andreas Chiocchetti | 2023-02-16 |
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
PCA, tSNE and UMAP from the first 30 dimensions and with a resolution of 0.8
# Visualization
Idents(samples.integrated) <- "seurat_clusters"
DimPlot(samples.integrated, reduction = "umap", split.by = "Treatment")
p1<- DimPlot(samples.integrated, reduction = "umap", group.by = "Plate")
p1a<- DimPlot(samples.integrated, reduction = "umap", group.by = "Mouse_ID")
p1b<- DimPlot(samples.integrated, reduction = "umap", group.by = "Genotype_Treatment")
p1 + p1a + p1b
DimPlot(samples.integrated, group.by = "seurat_clusters", pt.size =1, label = T) + NoLegend()
DimPlot(samples.integrated, reduction = "umap", split.by = "Plate", pt.size= 1)
DimPlot(samples.integrated, reduction = "umap", split.by = "Genotype_Treatment", pt.size =1)
Cell cycle scoring
First, a score is assigned to each cell (Tirosh et al. 2016), based on its expression of G2/M and S phase markers. These markers should be anticorrelated in their expression levels and cells expression neither are likely not cycling and in G1 phase. Note: For downstream cell cylce regression the quantitative scores for G2/M and S phase are used, not the dicrete classification.
Cluster distribution
DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by="Age",label = F) +
ggtitle("Cluster distribution according to Age") + theme_classic()| DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by="Genotype_Treatment",label = F) +
ggtitle("Cluster distribution according to Genotype + Treatment")+ theme_classic()
DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by="Plate",label = F) +
ggtitle("Cluster distribution according to Plate")+ theme_classic() | DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by = "seurat_clusters", label = T) +
ggtitle("Clustering")+ theme_classic()
Idents(samples.integrated) <- "seurat_clusters"
# use same colors for clusters as in plots
require(scales)
samples.integrated$seurat_clusters = factor(samples.integrated$seurat_clusters, levels=sort(as.character(unique(samples.integrated$seurat_clusters))))
identities <- levels(samples.integrated$seurat_clusters) # Create vector with levels of object@ident
cluster_colors <- hue_pal()(length(identities)) # Create vector of default ggplot2 colors
# number of cells in each cluster
cluster_nCell <- as.data.frame.matrix(table(samples.integrated$seurat_clusters,
samples.integrated$Genotype_Treatment))
cluster_nCell["Total" ,] = colSums(cluster_nCell)
cluster_nCell APPPS1_Ctrl APPPS1_Stroke WT_Ctrl WT_Stroke
0 164 38 108 62
1 28 15 28 15
2 10 5 42 0
3 0 0 4 0
4 0 0 8 0
5 6 1 5 3
6 1 4 7 8
Total 209 63 202 88# % of cells in each cluster , grouped by genotype_Age
cluster_per_Cell <- data.frame(table(samples.integrated$seurat_clusters,
samples.integrated$Age))
colnames(cluster_per_Cell) <- c("Cluster", "Age", "Frequency")
cluster_per_Cell Cluster Age Frequency
1 0 37 weeks 226
2 1 37 weeks 56
3 2 37 weeks 41
4 3 37 weeks 2
5 4 37 weeks 6
6 5 37 weeks 7
7 6 37 weeks 11
8 0 38 weeks 44
9 1 38 weeks 8
10 2 38 weeks 13
11 3 38 weeks 2
12 4 38 weeks 2
13 5 38 weeks 4
14 6 38 weeks 3
15 0 40 weeks 102
16 1 40 weeks 22
17 2 40 weeks 3
18 3 40 weeks 0
19 4 40 weeks 0
20 5 40 weeks 4
21 6 40 weeks 6cluster_percent_Cell <- data.frame(round((prop.table(x = table(samples.integrated$seurat_clusters,
samples.integrated$Age), margin = 2)*100),2))
colnames(cluster_percent_Cell) <- c("Cluster", "Age", "Frequency")
cluster_percent_Cell Cluster Age Frequency
1 0 37 weeks 64.76
2 1 37 weeks 16.05
3 2 37 weeks 11.75
4 3 37 weeks 0.57
5 4 37 weeks 1.72
6 5 37 weeks 2.01
7 6 37 weeks 3.15
8 0 38 weeks 57.89
9 1 38 weeks 10.53
10 2 38 weeks 17.11
11 3 38 weeks 2.63
12 4 38 weeks 2.63
13 5 38 weeks 5.26
14 6 38 weeks 3.95
15 0 40 weeks 74.45
16 1 40 weeks 16.06
17 2 40 weeks 2.19
18 3 40 weeks 0.00
19 4 40 weeks 0.00
20 5 40 weeks 2.92
21 6 40 weeks 4.38cluster_per_Treatment <- data.frame(table(samples.integrated$seurat_clusters,
samples.integrated$Genotype_Treatment))
colnames(cluster_per_Treatment) <- c("Cluster", "Genotype_Treatment", "Frequency")
cluster_per_Treatment Cluster Genotype_Treatment Frequency
1 0 APPPS1_Ctrl 164
2 1 APPPS1_Ctrl 28
3 2 APPPS1_Ctrl 10
4 3 APPPS1_Ctrl 0
5 4 APPPS1_Ctrl 0
6 5 APPPS1_Ctrl 6
7 6 APPPS1_Ctrl 1
8 0 APPPS1_Stroke 38
9 1 APPPS1_Stroke 15
10 2 APPPS1_Stroke 5
11 3 APPPS1_Stroke 0
12 4 APPPS1_Stroke 0
13 5 APPPS1_Stroke 1
14 6 APPPS1_Stroke 4
15 0 WT_Ctrl 108
16 1 WT_Ctrl 28
17 2 WT_Ctrl 42
18 3 WT_Ctrl 4
19 4 WT_Ctrl 8
20 5 WT_Ctrl 5
21 6 WT_Ctrl 7
22 0 WT_Stroke 62
23 1 WT_Stroke 15
24 2 WT_Stroke 0
25 3 WT_Stroke 0
26 4 WT_Stroke 0
27 5 WT_Stroke 3
28 6 WT_Stroke 8cluster_percent_Treatment <- data.frame(round((prop.table(x = table(samples.integrated$seurat_clusters,
samples.integrated$Genotype_Treatment), margin = 2)*100),2))
colnames(cluster_percent_Treatment) <- c("Cluster", "Genotype_Treatment", "Frequency")
cluster_percent_Treatment Cluster Genotype_Treatment Frequency
1 0 APPPS1_Ctrl 78.47
2 1 APPPS1_Ctrl 13.40
3 2 APPPS1_Ctrl 4.78
4 3 APPPS1_Ctrl 0.00
5 4 APPPS1_Ctrl 0.00
6 5 APPPS1_Ctrl 2.87
7 6 APPPS1_Ctrl 0.48
8 0 APPPS1_Stroke 60.32
9 1 APPPS1_Stroke 23.81
10 2 APPPS1_Stroke 7.94
11 3 APPPS1_Stroke 0.00
12 4 APPPS1_Stroke 0.00
13 5 APPPS1_Stroke 1.59
14 6 APPPS1_Stroke 6.35
15 0 WT_Ctrl 53.47
16 1 WT_Ctrl 13.86
17 2 WT_Ctrl 20.79
18 3 WT_Ctrl 1.98
19 4 WT_Ctrl 3.96
20 5 WT_Ctrl 2.48
21 6 WT_Ctrl 3.47
22 0 WT_Stroke 70.45
23 1 WT_Stroke 17.05
24 2 WT_Stroke 0.00
25 3 WT_Stroke 0.00
26 4 WT_Stroke 0.00
27 5 WT_Stroke 3.41
28 6 WT_Stroke 9.09# Grouped barchart of cell proportions
a1<- ggplot(cluster_percent_Cell, aes(fill=Age,
y=Frequency,
x=Cluster)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Age [%]") +theme_classic()+
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=1)) +
xlab("")
# Grouped barchart of cell proportions
b1 <- ggplot(cluster_percent_Cell, aes(fill=Cluster, y=Frequency, x=Age)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Age [%]") +theme_classic()+
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=1))
c1 <-ggplot(cluster_percent_Cell, aes(fill=Cluster, y=Frequency, x=Age)) +
geom_bar(position="stack", stat="identity")+theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=1))
a1 | b1 | c1
# Grouped barchart of cell proportions
a1<- ggplot(cluster_percent_Treatment, aes(fill=Genotype_Treatment,
y=Frequency,
x=Cluster)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Genotype_Treatment [%]") +theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=1)) +
xlab("")
# Grouped barchart of cell proportions
b1 <- ggplot(cluster_percent_Treatment, aes(fill=Cluster, y=Frequency, x=Genotype_Treatment)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Genotype_Treatment [%]") +theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=1))
c1 <-ggplot(cluster_percent_Treatment, aes(fill=Cluster, y=Frequency, x=Genotype_Treatment)) +
geom_bar(position="stack", stat="identity")+theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=1))
a1 | b1 | c1
Cell type identification
Finding differentially expressed features (cluster biomarkers)
Idents(samples.integrated) <- samples.integrated$seurat_clusters
samples.integrated=PrepSCTFindMarkers(samples.integrated)
markers <- FindAllMarkers(object = samples.integrated,
only.pos = TRUE,
logfc.threshold = 0.25)
DefaultAssay(samples.integrated) = "SCT"
markers %>%
group_by(cluster) %>%
top_n(n = 20, wt = avg_log2FC) -> top20
DoHeatmap(samples.integrated, features = top20$gene) + NoLegend() + ggtitle("Top20 cluster marker genes")
SingleR
SingleR is an automatic annotation method for (scRNAseq) data (Aran et al. 2019). Given a reference dataset of samples (single-cell or bulk) with known labels, it labels new cells from a test dataset based on similarity to the reference set. Here we use the built-in references “Immgen” (830 microarray samples of sorted hematopoetic and immune cell populations) and “Mouse RNA-Seq” (358 non-specific mouse RNA-seq samples).
Immgen reference
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
MouseRNA-Seq reference Section Skipped as pred.mouseRNA object is not available
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
# number of cells in each cluster
cluster_nCell <- data.frame(table(samples.integrated$MouseRNASeq_sc_labels,samples.integrated$Genotype_Treatment))
colnames(cluster_nCell) <- c( "MouseRNASeq_sc_labels", "Genotype_Treatment","Number")
#cluster_nCell["Total" ,] = colSums(cluster_nCell)
# cluster_nCell
# % of cells in each cluster
cluster_percent_Cell <- data.frame(round((prop.table(x = table(samples.integrated$MouseRNASeq_sc_labels,
samples.integrated$Genotype_Treatment), margin = 2)*100),2))
colnames(cluster_percent_Cell) <- c("MouseRNASeq_sc_labels", "Genotype_Treatment", "Frequency")
# cluster_percent_Cell
# Grouped barchart of absolute cell numbers
ggplot(cluster_nCell, aes(fill=Genotype_Treatment, y=Number, x=MouseRNASeq_sc_labels)) +
geom_bar(position="dodge", stat="identity") +
ggtitle("Cell distribution according to MouseRNASeq reference (absolut values)")+ theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
xlab("")
# Grouped barchart of cell proportions
ggplot(cluster_percent_Cell, aes(fill=Genotype_Treatment, y=Frequency, x=MouseRNASeq_sc_labels)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to MouseRNASeq reference [%]")+ theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
xlab("")
# number of cells in each cluster
cluster_nCell <- data.frame(table(samples.integrated$Immgen_sc_labels,samples.integrated$Genotype_Treatment))
colnames(cluster_nCell) <- c( "Immgen_sc_labels", "Genotype_Treatment","Number")
#cluster_nCell["Total" ,] = colSums(cluster_nCell)
# cluster_nCell
# % of cells in each cluster
cluster_percent_Cell <- data.frame(round((prop.table(x = table(samples.integrated$Immgen_sc_labels,
samples.integrated$Genotype_Treatment), margin = 2)*100),2))
colnames(cluster_percent_Cell) <- c( "Immgen_sc_labels", "Genotype_Treatment","Frequency")
# cluster_percent_Cell
# Grouped barchart of absolute cell numbers
ggplot(cluster_nCell, aes(fill=Genotype_Treatment, y=Number, x=Immgen_sc_labels)) +
geom_bar(position="dodge", stat="identity") +
ggtitle("Cell distribution according to Immgen reference (absolut values)") + theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
xlab("")
# Grouped barchart of cell proportions
ggplot(cluster_percent_Cell, aes(fill=Genotype_Treatment, y=Frequency, x=Immgen_sc_labels)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Immgen reference [%]") + theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
xlab("")
Renaming Clusters
Idents(samples.integrated) = "Celltype"
DimPlot(samples.integrated, reduction = "umap", label=T) + NoLegend()+ theme_classic()
Zoom into Microglia clusters
A second Seurat cluster is generated, where all non-microglial immune cells are removed according to the mouseRNASeq_sc_labels.
samples.microglia <- samples.integrated[,grepl("Microgli.*", samples.integrated$MouseRNASeq_sc_labels)]
p3 <- DimPlot(samples.microglia, reduction = "umap", label=T, group.by = "MouseRNASeq_sc_labels") + theme_classic()+ NoLegend()
p3a <- DimPlot(samples.microglia, reduction = "umap", label=T, group.by = "seurat_clusters") + theme_classic()+ NoLegend()
p3 + p3a
DAMs (Cell. 2017 Jun 15;169(7):1276-1290.e17. doi: 10.1016/j.cell.2017.05.018)
DAM plotting
DAM2_marker_gene_list <- list(Stage2_DAM_up)
samples.microglia <- AddModuleScore(object = samples.microglia,
features = DAM2_marker_gene_list, name = "DAM2_score")
p5 <- FeaturePlot(object = samples.microglia, features = "DAM2_score1")+scale_color_viridis_c()Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.p5a <- DimPlot(object = samples.microglia, reduction = "umap")
p5 | p5a
DAM 2 Markers
DAM2 upreagulated markers
DAM2 dowreagulated markers
points of interest spatial sequencing
Special Seq plotting
Renaming clusters
Idents(samples.microglia) <- "Celltype"
DimPlot(samples.microglia, reduction = "umap", label = TRUE, pt.size = 1)+ NoLegend()
Signature enrichment
All enrichment tests were done with gprofiler 2
Tested Ontologies and Signatures GO:MF = Molecular Function GO:BP = Biological Processes GO:CC = Cellular Compartment), KEGG = pathways from KEGG Reactome, TF = regulatory motif matches from TRANSFAC HPA = tissue specificity from Human Protein Atlas; CORUM = protein complexes from CORUM HP = human disease phenotypes from Human Phenotype Ontology.
For more dteails see Website: https://biit.cs.ut.ee/gprofiler/gost”
DEGs and GSEA across microglia of different genotypes
log fold-change of the average expression between the two groups: Positive values indicate that the gene is more highly expressed in the target group (e.g. the APPPS1+ group)
WT vs MX04+
log fold-chage of the average expression between the two groups: Positive values indicate that the gene is more highly expressed in the Methoxy positive group
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|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
MX04+ vs MX04- in APPPS1 cohort only
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| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
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WT_Ctrl vs WT_Stroke
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| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
DEGs and GSEA of each cluster
log fold-chage of the average expression between the two groups: Positive values indicate that the gene is more highly expressed in the target group (e.g. Microglia 0) versus all others.
Spatial Subgroup Controls
Genotype + Treatment comparison in each cluster
Idents(samples.microglia) <- "Genotype_Treatment"
mg1 <- samples.microglia[,samples.microglia$Celltype == "Microglia_1"]
mg1 <- SCTransform(mg1)Calculating cell attributes from input UMI matrix: log_umiVariance stabilizing transformation of count matrix of size 23747 by 371Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 371 cells
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|======================================================================| 100%There are 1 estimated thetas smaller than 1e-07 - will be set to 1e-07Found 3 outliers - those will be ignored in fitting/regularization stepSecond step: Get residuals using fitted parameters for 23747 genes
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|======================================================================| 100%Computing corrected count matrix for 23747 genes
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|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 4.460372 secsDetermine variable featuresPlace corrected count matrix in counts slotCentering data matrixSet default assay to SCTmg1.markers.Genotype_Treatment <- FindAllMarkers(mg1)Calculating cluster WT_StrokeCalculating cluster APPPS1_CtrlCalculating cluster APPPS1_StrokeCalculating cluster WT_Ctrlif(length(mg1.markers.Genotype_Treatment)!=0){
mg1_top20 = mg1.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
mg1_top20chk=T
} else {mg1_top20chk=F}
mg2 <- samples.microglia[,samples.microglia$Celltype == "Microglia_2"]
mg2 <- SCTransform(mg2)Calculating cell attributes from input UMI matrix: log_umiVariance stabilizing transformation of count matrix of size 15372 by 86Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 86 cells
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|======================================================================| 100%There are 1 estimated thetas smaller than 1e-07 - will be set to 1e-07Found 34 outliers - those will be ignored in fitting/regularization stepSecond step: Get residuals using fitted parameters for 15372 genes
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|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 1.91747 secsDetermine variable featuresPlace corrected count matrix in counts slotCentering data matrixSet default assay to SCTmg2.markers.Genotype_Treatment <- FindAllMarkers(mg2)Calculating cluster WT_StrokeCalculating cluster APPPS1_CtrlCalculating cluster APPPS1_StrokeCalculating cluster WT_Ctrlif(length(mg2.markers.Genotype_Treatment)!=0){
mg2_top20 = mg2.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
mg2_top20chk=T
} else {mg2_top20chk=F}
mg3 <- samples.microglia[,samples.microglia$Celltype == "Microglia_3"]
mg3 <- SCTransform(mg3)Calculating cell attributes from input UMI matrix: log_umiVariance stabilizing transformation of count matrix of size 17416 by 57Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 57 cells
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|======================================================================| 100%There are 4 estimated thetas smaller than 1e-07 - will be set to 1e-07Found 36 outliers - those will be ignored in fitting/regularization stepSecond step: Get residuals using fitted parameters for 17416 genes
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|======================================================================| 100%Computing corrected count matrix for 17416 genes
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|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 1.856266 secsDetermine variable featuresPlace corrected count matrix in counts slotCentering data matrixSet default assay to SCTmg3.markers.Genotype_Treatment <- FindAllMarkers(mg3)Calculating cluster APPPS1_CtrlCalculating cluster APPPS1_StrokeCalculating cluster WT_Ctrlif(length(mg3.markers.Genotype_Treatment)!=0){
mg3_top20 = mg3.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
mg3_top20chk=T
} else {mg3_top20chk=F}
mg4 <- samples.microglia[,samples.microglia$Celltype == "Microglia_4"]
mg4 <- SCTransform(mg4)Calculating cell attributes from input UMI matrix: log_umiVariance stabilizing transformation of count matrix of size 2188 by 8Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 8 cells
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|======================================================================| 100%There are 25 estimated thetas smaller than 1e-07 - will be set to 1e-07Found 47 outliers - those will be ignored in fitting/regularization stepSecond step: Get residuals using fitted parameters for 2188 genes
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|======================================================================| 100%Computing corrected count matrix for 2188 genes
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|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 1.100552 secsDetermine variable featuresPlace corrected count matrix in counts slotCentering data matrixSet default assay to SCTmg4.markers.Genotype_Treatment <- FindAllMarkers(mg4)Calculating cluster WT_CtrlWarning: No DE genes identifiedWarning: The following tests were not performed:Warning: When testing WT_Ctrl versus all:
Cell group 2 is empty - no cells with identity class if(length(mg4.markers.Genotype_Treatment)!=0){
mg4_top20 = mg4.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
mg4_top20chk=T
} else {mg4_top20chk=F}
mg5 <- samples.microglia[,samples.microglia$Celltype == "Microglia_5"]
mg5 <- SCTransform(mg5)Calculating cell attributes from input UMI matrix: log_umiVariance stabilizing transformation of count matrix of size 5172 by 15Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 15 cells
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|======================================================================| 100%Second step: Get residuals using fitted parameters for 5172 genes
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|======================================================================| 100%Computing corrected count matrix for 5172 genes
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|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 1.203822 secsDetermine variable featuresPlace corrected count matrix in counts slotCentering data matrixSet default assay to SCTmg5.markers.Genotype_Treatment <- FindAllMarkers(mg5)Calculating cluster WT_StrokeCalculating cluster APPPS1_CtrlCalculating cluster APPPS1_StrokeCalculating cluster WT_CtrlWarning: The following tests were not performed:Warning: When testing APPPS1_Stroke versus all:
Cell group 1 has fewer than 3 cellsif(length(mg5.markers.Genotype_Treatment)!=0){
mg5_top20 = mg5.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
mg5_top20chk=T
} else {mg5_top20chk=F}
sp_ctrl <- samples.microglia[,samples.microglia$Control_spatial_binary==T]
sp_ctrl <- SCTransform(sp_ctrl)Calculating cell attributes from input UMI matrix: log_umiVariance stabilizing transformation of count matrix of size 11668 by 44Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 44 cells
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|======================================================================| 100%There are 1 estimated thetas smaller than 1e-07 - will be set to 1e-07Found 2 outliers - those will be ignored in fitting/regularization stepSecond step: Get residuals using fitted parameters for 11668 genes
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|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 1.5381 secsDetermine variable featuresPlace corrected count matrix in counts slotCentering data matrixSet default assay to SCTsp_ctrl.markers.Genotype_Treatment <- FindAllMarkers(sp_ctrl)Calculating cluster WT_StrokeCalculating cluster APPPS1_CtrlCalculating cluster APPPS1_StrokeCalculating cluster WT_Ctrlif(length(sp_ctrl.markers.Genotype_Treatment)!=0){
sp_ctrl_top20 = sp_ctrl.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
sp_ctrl_top20chk=T
} else {sp_ctrl_top20chk=F}
sp_stroke <- samples.microglia[,samples.microglia$Stroke_spatial_binary==T]
sp_stroke <- SCTransform(sp_stroke)Calculating cell attributes from input UMI matrix: log_umiVariance stabilizing transformation of count matrix of size 19085 by 170Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 170 cells
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|======================================================================| 100%There are 1 estimated thetas smaller than 1e-07 - will be set to 1e-07Found 3 outliers - those will be ignored in fitting/regularization stepSecond step: Get residuals using fitted parameters for 19085 genes
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|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 2.530094 secsDetermine variable featuresPlace corrected count matrix in counts slotCentering data matrixSet default assay to SCTsp_stroke.markers.Genotype_Treatment <- FindAllMarkers(sp_stroke)Calculating cluster WT_StrokeCalculating cluster APPPS1_CtrlCalculating cluster APPPS1_StrokeCalculating cluster WT_CtrlWarning: The following tests were not performed:Warning: When testing WT_Ctrl versus all:
Cell group 1 has fewer than 3 cellsif(length(sp_stroke.markers.Genotype_Treatment)!=0){
sp_stroke_top20 = sp_stroke.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
sp_stroke_top20chk=T
} else {sp_stroke_top20chk=F}Microglia_1
Microglia_2
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[1] "no significant markers identified"Microglia_5
spatial Controls
spatial Stroke
save.image("./output/integrated_analysis.RData")
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] DESeq2_1.38.3 scales_1.2.1
[3] gprofiler2_0.2.1 forcats_1.0.0
[5] stringr_1.5.0 dplyr_1.1.0
[7] purrr_1.0.1 readr_2.1.4
[9] tidyr_1.3.0 tibble_3.1.8
[11] tidyverse_1.3.2 pheatmap_1.0.12
[13] EnhancedVolcano_1.16.0 ggrepel_0.9.3
[15] SingleR_2.0.0 SummarizedExperiment_1.28.0
[17] Biobase_2.58.0 GenomicRanges_1.50.2
[19] GenomeInfoDb_1.34.9 IRanges_2.32.0
[21] S4Vectors_0.36.1 BiocGenerics_0.44.0
[23] MatrixGenerics_1.10.0 matrixStats_0.63.0
[25] ggplot2_3.4.1 SeuratObject_4.1.3
[27] Seurat_4.3.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] scattermore_0.8 bit64_4.0.5
[3] knitr_1.42 irlba_2.3.5.1
[5] DelayedArray_0.24.0 data.table_1.14.6
[7] KEGGREST_1.38.0 RCurl_1.98-1.10
[9] generics_0.1.3 ScaledMatrix_1.6.0
[11] callr_3.7.3 cowplot_1.1.1
[13] RSQLite_2.2.20 RANN_2.6.1
[15] future_1.31.0 bit_4.0.5
[17] tzdb_0.3.0 spatstat.data_3.0-0
[19] xml2_1.3.3 lubridate_1.9.2
[21] httpuv_1.6.9 assertthat_0.2.1
[23] gargle_1.3.0 xfun_0.37
[25] hms_1.1.2 jquerylib_0.1.4
[27] evaluate_0.20 promises_1.2.0.1
[29] fansi_1.0.4 dbplyr_2.3.0
[31] readxl_1.4.2 igraph_1.4.0
[33] DBI_1.1.3 geneplotter_1.76.0
[35] htmlwidgets_1.6.1 spatstat.geom_3.0-6
[37] googledrive_2.0.0 ellipsis_0.3.2
[39] crosstalk_1.2.0 backports_1.4.1
[41] annotate_1.76.0 deldir_1.0-6
[43] sparseMatrixStats_1.10.0 vctrs_0.5.2
[45] ROCR_1.0-11 abind_1.4-5
[47] cachem_1.0.6 withr_2.5.0
[49] progressr_0.13.0 sctransform_0.3.5
[51] goftest_1.2-3 cluster_2.1.4
[53] lazyeval_0.2.2 crayon_1.5.2
[55] spatstat.explore_3.0-6 pkgconfig_2.0.3
[57] labeling_0.4.2 nlme_3.1-162
[59] vipor_0.4.5 rlang_1.0.6
[61] globals_0.16.2 lifecycle_1.0.3
[63] miniUI_0.1.1.1 modelr_0.1.10
[65] rsvd_1.0.5 ggrastr_1.0.1
[67] cellranger_1.1.0 rprojroot_2.0.3
[69] polyclip_1.10-4 lmtest_0.9-40
[71] Matrix_1.5-3 zoo_1.8-11
[73] reprex_2.0.2 beeswarm_0.4.0
[75] whisker_0.4.1 ggridges_0.5.4
[77] processx_3.8.0 googlesheets4_1.0.1
[79] png_0.1-8 viridisLite_0.4.1
[81] bitops_1.0-7 getPass_0.2-2
[83] KernSmooth_2.23-20 Biostrings_2.66.0
[85] blob_1.2.3 DelayedMatrixStats_1.20.0
[87] parallelly_1.34.0 spatstat.random_3.1-3
[89] beachmat_2.14.0 memoise_2.0.1
[91] magrittr_2.0.3 plyr_1.8.8
[93] ica_1.0-3 zlibbioc_1.44.0
[95] compiler_4.2.2 RColorBrewer_1.1-3
[97] fitdistrplus_1.1-8 cli_3.6.0
[99] XVector_0.38.0 listenv_0.9.0
[101] patchwork_1.1.2 pbapply_1.7-0
[103] ps_1.7.2 MASS_7.3-58.2
[105] tidyselect_1.2.0 stringi_1.7.12
[107] highr_0.10 yaml_2.3.7
[109] BiocSingular_1.14.0 locfit_1.5-9.7
[111] grid_4.2.2 sass_0.4.5
[113] tools_4.2.2 timechange_0.2.0
[115] future.apply_1.10.0 parallel_4.2.2
[117] rstudioapi_0.14 git2r_0.31.0
[119] gridExtra_2.3 farver_2.1.1
[121] Rtsne_0.16 digest_0.6.31
[123] shiny_1.7.4 Rcpp_1.0.10
[125] broom_1.0.3 later_1.3.0
[127] RcppAnnoy_0.0.20 httr_1.4.4
[129] AnnotationDbi_1.60.0 colorspace_2.1-0
[131] rvest_1.0.3 XML_3.99-0.13
[133] fs_1.6.1 tensor_1.5
[135] reticulate_1.28 splines_4.2.2
[137] uwot_0.1.14 spatstat.utils_3.0-1
[139] sp_1.6-0 plotly_4.10.1
[141] xtable_1.8-4 jsonlite_1.8.4
[143] R6_2.5.1 pillar_1.8.1
[145] htmltools_0.5.4 mime_0.12
[147] DT_0.27 glue_1.6.2
[149] fastmap_1.1.0 BiocParallel_1.32.5
[151] codetools_0.2-19 utf8_1.2.3
[153] lattice_0.20-45 bslib_0.4.2
[155] spatstat.sparse_3.0-0 ggbeeswarm_0.7.1
[157] leiden_0.4.3 zip_2.2.2
[159] openxlsx_4.2.5.2 survival_3.5-3
[161] limma_3.54.1 rmarkdown_2.20
[163] munsell_0.5.0 GenomeInfoDbData_1.2.9
[165] haven_2.5.1 reshape2_1.4.4
[167] gtable_0.3.1